
from npu_bridge.npu_init import *
import cv2
import time
import numpy as np
import core.utils as utils
import tensorflow as tf
from PIL import Image
return_elements = ['input/input_data:0', 'pred_sbbox/concat_2:0', 'pred_mbbox/concat_2:0', 'pred_lbbox/concat_2:0']
pb_file = './yolov3_coco.pb'
video_path = './docs/images/road.mp4'
num_classes = 80
input_size = 416
graph = tf.Graph()
return_tensors = utils.read_pb_return_tensors(graph, pb_file, return_elements)
with tf.Session(graph=graph) as sess:
    vid = cv2.VideoCapture(video_path)
    while True:
        (return_value, frame) = vid.read()
        if return_value:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            image = Image.fromarray(frame)
        else:
            raise ValueError('No image!')
        frame_size = frame.shape[:2]
        image_data = utils.image_preporcess(np.copy(frame), [input_size, input_size])
        image_data = image_data[(np.newaxis, ...)]
        prev_time = time.time()
        (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run([return_tensors[1], return_tensors[2], return_tensors[3]], feed_dict={return_tensors[0]: image_data})
        pred_bbox = np.concatenate([np.reshape(pred_sbbox, ((- 1), (5 + num_classes))), np.reshape(pred_mbbox, ((- 1), (5 + num_classes))), np.reshape(pred_lbbox, ((- 1), (5 + num_classes)))], axis=0)
        bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size, 0.3)
        bboxes = utils.nms(bboxes, 0.45, method='nms')
        image = utils.draw_bbox(frame, bboxes)
        curr_time = time.time()
        exec_time = (curr_time - prev_time)
        result = np.asarray(image)
        info = ('time: %.2f ms' % (1000 * exec_time))
        cv2.namedWindow('result', cv2.WINDOW_AUTOSIZE)
        result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        cv2.imshow('result', result)
        if ((cv2.waitKey(1) & 255) == ord('q')):
            break
